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Dynamic metabolic modeling of a microaerobic yeast co-culture: predicting and optimizing ethanol production from glucose/xylose mixtures

机译:微需氧酵母共培养物的动态代谢建模:预测和优化葡萄糖/木糖混合物中乙醇的产量

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Background A key step in any process that converts lignocellulose to biofuels is the efficient fermentation of both hexose and pentose sugars. The co-culture of respiratory-deficient Saccharomyces cerevisiae and wild-type Scheffersomyces stipitis has been identified as a promising system for microaerobic ethanol production because S. cerevisiae only consumes glucose while S. stipitis efficiently converts xylose to ethanol. Results To better predict how these two yeasts behave in batch co-culture and to optimize system performance, a dynamic flux balance model describing co-culture metabolism was developed from genome-scale metabolic reconstructions of the individual organisms. First a dynamic model was developed for each organism by estimating substrate uptake kinetic parameters from batch pure culture data and evaluating model extensibility to different microaerobic growth conditions. The co-culture model was constructed by combining the two individual models assuming a cellular objective of total growth rate maximization. To obtain accurate predictions of batch co-culture data collected at different microaerobic conditions, the S. cerevisiae maximum glucose uptake rate was reduced from its pure culture value to account for more efficient S. stipitis glucose uptake in co-culture. The dynamic co-culture model was used to predict the inoculum concentration and aeration level that maximized batch ethanol productivity. The model predictions were validated with batch co-culture experiments performed at the optimal conditions. Furthermore, the dynamic model was used to predict how engineered improvements to the S. stipitis xylose transport system could improve co-culture ethanol production. Conclusions These results demonstrate the utility of the dynamic co-culture metabolic model for guiding process and metabolic engineering efforts aimed at increasing microaerobic ethanol production from glucose/xylose mixtures.
机译:背景技术在将木质纤维素转化为生物燃料的任何过程中的关键步骤是己糖和戊糖的有效发酵。呼吸缺陷型酿酒酵母和野生型啤酒糖酵母的共培养已被确定为有氧乙醇生产的有前途的系统,因为啤酒酵母仅消耗葡萄糖,而啤酒糖有效地将木糖转化为乙醇。结果为了更好地预测这两种酵母在分批共培养中的行为并优化系统性能,从单个生物的基因组规模代谢重建中开发了描述共培养代谢的动态通量平衡模型。首先,通过从批量纯培养数据估计底物吸收动力学参数,并评估模型对不同微需氧生长条件的可扩展性,为每种生物开发一个动力学模型。假设将总生长速率最大化的细胞目标,则通过组合两个单独的模型来构建共培养模型。为了获得在不同的有氧条件下收集的分批共培养数据的准确预测,啤酒酵母的最大葡萄糖摄取率已从其纯培养值中降低,以说明共培养过程中更有效的树干葡萄球菌葡萄糖摄取。动态共培养模型用于预测最大批量乙醇生产量的接种物浓度和通气水平。通过在最佳条件下进行的分批共培养实验验证了模型预测。此外,使用动态模型来预测改良的化学改良方法,从而可以改善共培养乙醇的产量。结论这些结果表明,动态共培养代谢模型可用于指导过程和代谢工程,以增加葡萄糖/木糖混合物中的微需氧乙醇的生产。

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